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Personal learning environments or their acronym PLEs are understood as a set of tools, resources, connections, and activities that each person regularly uses for learning. This study examines the PLE approach in a university setting, evaluating its impact on student learning in a university subject within an education master's program. The effect is assessed from two perspectives: a quantitative one based on the 'PLE test'—administered at the beginning and end of the course—and a qualitative one based on the assessment provided by the students themselves regarding the impact of training in digital tools and competencies on their PLE, at the beginning and end of the course. The 'PLE test' measures four factors: time organization, creation–editing, searching–investigating, and collaborating–contact networks. In all four factors, by the end of the course, there is a significant increase in usage scores, especially in the last two. The students' assessments of the evolution of their PLEs reflect the evident impact of this approach as a strategy for more effective and self-managed learning. [ABSTRACT FROM AUTHOR]

In this paper, for self‐driving forklift, a path‐following framework based on composite disturbance rejection that combines a cascaded active disturbance rejection controller and an online estimator of model parameters is proposed on the basis of a geometry feedforward controller. The model‐based cascaded active disturbance rejection control (ADRC), in particular, is composed of an outer‐loop controller and an inner‐loop controller, with the outer‐loop controller reducing lateral error by manipulating the desired heading direction and the inner‐loop controller achieving heading control by adjusting the steering angle. The unmodeled dynamics of the actual forklift motion are denoted as total disturbances, which can be timely estimated by the extended state observer (ESO). Moreover, to increase the transient response, a parameter self‐learning based geometry feedforward controller is designed in conjunction with the forklift characteristics. The stability analysis is presented and the effectiveness of the composite algorithm is quantitatively evaluated in experiments, demonstrating the superiority over the conventional pure pursuit algorithm and the only use of ADRC. [ABSTRACT FROM AUTHOR]

This study explores the use of open educational resources (OERs) in studio-based learning and their influence on learning experiences. The research team conducted action research with 30 bachelor of arts students who were completing a video production subject. Students were required to learn from a website containing open online learning resources under a flipped classroom approach. A teaching schedule and website were designed according to several criteria. Research data were collected through observation, reflective journals, and interviews and were analyzed via thematic analysis. Participating students expressed their perceptions of benefits and hesitation in utilizing OERs in learning. They agreed that the use of OERs as flipped classroom learning materials could positively affect their learning, primarily through competence and learning autonomy as indicated in self-determination theory. This investigation provides teachers with valuable experience and suggestions for teaching and learning approaches that incorporate OERs into studio-based education. Students learn from OERs in which they can gain the most up-to-date technical knowledge in an autonomous environment. This experience indicates that this pedagogy greatly and positively influences students' subject-learning experiences, learning outcomes, and self-learning skills. [ABSTRACT FROM AUTHOR]

Educating is not transmitting advances in science and technology to students, but developing in them a scientific conception of the world, about society, nature, human beings, thought and about themselves, which becomes convictions and core of his training. One of the objectives of the research is to recognize the pedagogical category training as a process and result, currently the assimilation of scientific knowledge of contemporary times in the formation of a conception and scientific attitude towards the phenomena of reality, by students. from a scientific thought with a responsible attitude towards knowledge and employment based on social good, which is achieved through the unity between the scientific character and the intentionality of education. The teaching practices are designed to train our teachers as future competent professionals who contribute to raising the quality of an education with responsibility. For this, the use of practical theoretical methods is proposed that allows us to verify the importance of implementing the study-work principle in our young people in training. [ABSTRACT FROM AUTHOR]

Pan-sharpening is a very productive technique to enhance the spatial details of multispectral images with the aid of panchromatic images. Nowadays, deep learning-based pan-sharpening has scored tremendous achievements. However, strict requirement for training image pairs and low generalization hamper the development of supervised pan-sharpening with limited samples. Unsupervised image fusion is an effective technique to fuse images without adequate ground truth as training samples. Existing unsupervised pan-sharpening methods are commonly based on the image generator model, suffering from unsatisfactory spatial details. In this study, we proposed a self-supervised pan-sharpening method with the aid of spectral super-resolution named sSRPNet. Coarsening-scale self-learning exploits the internal information in multispectral images at a coarsening scale and trains the initial fusion model without other labels. Spectral super-resolution injection explores the missing spatial details in the initial fused images and recovers it. Degradation self-learning introduces strong spectral constraints with original multispectral images. Both reduced-resolution and full-resolution experiments on three datasets have proved the superiority of sSRPNet. Furthermore, the proposed spectral super-resolution injection can be implemented on any existing pan-sharpening algorithms, improving their performance. [ABSTRACT FROM AUTHOR]

Particle swarm optimization has been widely utilized to tackle various real optimization problems as an effective and simple optimization approach. However, the phenomenon of premature convergence has always existed. To ameliorate the drawback, a self-learning particle swarm optimization algorithm with multi-strategy selection (MSLSPSO) is proposed in this study. In MSLSPSO, an elite particles guidance strategy is proposed. This strategy makes elite particles participate in the search process to strengthen the guidance of the population. A Lévy Flight perturbation strategy is designed, which utilizes the random walk characteristic of Lévy Flight to perturb the historical and global optimal of particles, increasing the diversity of the population. In addition, a fitness distance correlation self-learning mechanism is proposed, which can self-learning according to the characteristics of the population in the search process. A set of test functions are utilized for experimental analysis and compared with four well-known PSO variants. Experimental results show that the MSLSPSO algorithm has efficient robustness and competitive solutions can be obtained. [ABSTRACT FROM AUTHOR]

The sports world has many prejudices that have been converted into common sense. Some relate to the idea of athletes being strong or pretty but endowed with little intelligence. There is another view, perhaps a little more accurate, around the reification of consciousness in the name of the automation and maximum outcome of the body. Both views are informed by Cartesian thinking, perpetuating the mind–body dichotomy. Such a dichotomy is spread in several other areas in our society, expressed as binaries. We meet a binary when conducting research as well, disembodying the researcher as someone who is neutral, objective, and highly rational, and someone who, in synthesis, performs good mental work, but who must not let feelings intrude. On the contrary, we argue that we are embodied beings who are often not able to (and maybe should not) become detached from previous experiences and knowledge when conducting research. Even though this can present itself as a challenge, we consider that a fluid non-binary positioning encompasses actions holistically and leads to tasks being performed on a continuum. The purpose of this paper is to explore the reflexive process embedded in carrying out a PhD project committed to studying the production of the embodied subjectivities of a group of women high-level athletes in karate. The researcher inserted in the researched environment was not a high-level athlete; however, she had several experiences competing at the amateur level in different countries and faced experiences that were, to some extent, similar to those of the elite athletes. She used her previous experiences as a karateka, researcher, and woman to inform her research-doing since the intersectional social issues faced by her and lived queer feelings motivated her research questions. She plunged into a process of self-reflection and counted on the guidance of the other authors to organise her learning in order to use it in her scholarship. That was, primarily, an experience of "practice" of subjectivity through examining others' production of subjectivity, besides strengthening a positionality that lacked self-confidence. Thus, we explore issues around the researcher–practitioner theoretical–practical continuum of research-doing, presenting a journey that became empowering. [ABSTRACT FROM AUTHOR]

Existing medical image processing models based on Transformers primarily rely on self-attention mechanisms to capture short-range and long-range visual dependencies. However, this approach has limitations in modeling the global context of full-resolution images, resulting in the loss of significant details. In order to address these issues, we propose an Extensible Attentional Self-learning Transformer (EAST) architecture for medical image segmentation. In EAST, tokenized images are input into an extensible attention module, enabling the training of multi-scale representations that effectively capture both fine-grained local interactions and coarse-grained global relationships. This allows for more comprehensive learning of semantic information. The obtained features are then passed through a self-learning module, further refining the representations of different samples to generate a more accurate feature map. To handle high-resolution images, the EAST architecture utilizes a U-shaped structure and skip connections for sequential processing of extensible attention feature maps. Experimental results on the Synapse dataset and ACDC dataset demonstrate the superior performance of our EAST architecture compared to other methods. Additionally, the EAST model is capable of capturing more detailed information, leading to precise localization of structures. [ABSTRACT FROM AUTHOR]

Copyright of University of Sharjah Journal for Humanities & Social Sciences is the property of University of Sharjah - Scientific Publishing Unit and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Lightning is one of the main causes of voltage sags in power grid. Accurate estimation of the severity of voltage sags caused by lightning can provide a basis for developing optimal management plans and siting sensitive users. In this paper, a data-driven self-learning estimation method for the severity of voltage sags is proposed. Firstly, based on the mechanism of voltage sags caused by lightning, the parameters involved in mining are selected by the monitoring information in lightning location system and power quality monitoring system. Secondly, the influence of discretization results on the accuracy of rules is reduced, and the number of discretization intervals for different parameters is determined by using discretization evaluation indexes. Then, to solve the problem of low efficiency of mining algorithm when the grid database changes dynamically, the incremental learning-based association rule mining algorithm to continuously update the mined rules, which gives it the ability of self-learning. Finally, a weighted Euclidean distance based on the integrated assignment method is proposed to evaluate the severity of voltage sags in real scenarios. The results of the empirical analysis by monitoring data of a regional power grid and simulation data of IEEE 30-node prove that the method in this paper can accurately mine valuable rules in reality and realize the severity assessment of voltage sags of the concerned nodes.

Objective To explore the application effect of the flipped classroom with ideological-political in the molecular biology course. Methods A comparative study was conducted with undergraduates majored in clinical medicine from eight classes in grade 2019 as subjects, who were divided into control group using traditional teaching method containing ideological-political elements and the experimental group adopting the flipped classroom with ideological-political elements. Questionnaires, college students' self-learning ability assessment scale and an examination were used to evaluate the satisfaction of teaching mode, the improvement of students' self-learning ability and the teaching effects in the two groups. Results Compared with the control group, the performance of the students in the experiment group was statistically better than the control group (P＜0. 05) in terms of mastering the classroom knowledge, inspiring the interest in learning, enhancing the capacity of analysis and solving problems and teamwork, impoving class participation and guidance of ideological-political elements on future career planning. The self-learning ability of students in the experimental group was obviously improved. The theoretical test score of the experimental group was significantly higher than the control group (P＜0.05). Conclusions The application of flipped classroom with ideological-political elements in teaching molecular biology for the undergraduates can improve their performance skill and capacity building of self-learning.

The drive train is one of the core structures of a wind turbine, and its working condition seriously affects the performance quality. It is important to identify the fault pattern of the drive bearings in time to ensure the safety and reliability of the wind turbine. However, in traditional methods, offline modeling and online identification are often fragmented, and such a mechanism limits the adaptive updating of the model. To realize real-time updating and self-learning of the identification model, we proposed a novel self-learning framework for the intelligent fault identification of wind turbine drive bearings. First, a complete ensemble empirical mode decomposition with adaptive noise analysis–based quantification scheme for intrinsic mode function values is proposed. Then, based on the intrinsic mode function values, we offer an attention mechanism for fault feature identification and construct an initial fault pattern database using unsupervised clustering techniques. Second, abnormal data are identified by the proposed artificial immunity–based outlier detection algorithm to determine the type of immune response. Third, we design an automatic update strategy based on incremental learning to realize adaptive creation, deletion, and modification of fault patterns. The proposed intelligent framework is applied to the fault diagnosis of a real offshore wind turbine drive train, showing its advantages in intelligent fault identification and model updating. [ABSTRACT FROM AUTHOR]

At present, different investigations reveal self-learning as a continuous process, factors such as sleep, reading, music and physical activities that also include leisure and recreation, favor and stimulate our attention levels to correctly capture the different information that the world offers us. medium where we develop. Neuroscience as a subject is essential for the study of the human being since education and psychology are integrated in order to improve the didactic resources for the training of students. The objective of this research is to reflect on the application of this science in education as a perfect combination for academic success. A methodology with a qualitative approach was applied and scientific research methods such as theoretical and empirical were used, which allowed to inquire about neuroscience and neuroeducation in the various fields of education. The results demonstrate the interesting field of neurosciences since each brain is unique and that learning is more effective when it is emotionally significant. In conclusion, this science allows us to know how the nervous system works to produce and regulate emotions, thoughts, behaviors: understand how emotions influence the teaching-learning process, which currently and in the future allows improving teaching methodologies. [ABSTRACT FROM AUTHOR]

Aim: To test a modified team‐based learning approach on undergraduate learning outcomes in an acute‐care nursing course in Japan. Design: Mixed‐methods. Methods: Students worked on three simulated cases, engaged in pre‐class preparation, completed a quiz and engaged in group work. We collected data on team approach, critical‐thinking disposition and time spent in self‐learning at four time‐points: before the intervention and after each simulated case. Data were analysed using a linear mixed model, a Kruskal–Wallis test and a content analysis. Data sources: We recruited nursing students attending a mandatory course in acute‐care nursing at University A. Data were collected at four time‐points between April and July 2018. Data from 73 of 93 respondents were analysed. Results: Team approach, critical thinking and self‐learning all increased significantly across the time‐points. Four categories emerged from students' comments: 'achievement of teamwork', 'sense of learning efficacy', 'satisfaction with course approach' and 'issues related to course approach'. The modified team‐based learning approach led to improvements in team approach and critical‐thinking disposition across the course. Conclusion: Incorporating team‐based learning into the curriculum not only contributes to team building but is also effective as a teaching method to improve student learning. Implications for the profession and/or patient care: The intervention led to improvements in team approach and critical‐thinking disposition across the course. The educational intervention also led to more time for self‐learning. Future studies should include participants from various universities and evaluate the outcomes over a longer period. [ABSTRACT FROM AUTHOR]

This paper evaluates the consumption motivation regarding online learning. The research aims to gather information regarding learning engagement, motivation, satisfaction, and barriers to accessing and completing online courses. Grasping the patterns of digital course participants is vital for academic bodies and decision-makers to create and advocate for engaging online educational journeys. Collecting data concerning various factors aids in pinpointing consumer categories according to their necessities. [ABSTRACT FROM AUTHOR]

Copyright of Revista Latinoamericana de Tecnología Educativa is the property of Revista Latinoamericana de Tecnologia Educativa - RELATEC and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Data structures and Algorithms (DSA) is a mandatory course for all discipline students to get placement in IT companies and to participate in competitive examinations including GATE and TANCET for their higher studies in a computer science discipline. DSA course focuses on how to organize, manage and store data in an efficient manner, which facilitates to access data easily and at a faster rate. Different types of data structures, its functionality and its applicability are discussed in this course. At the end of the course, students will have the capability to identify the suitable data structure for a problem. Due to its importance and complexity, pressure will be created on faculty members, who are handling this course. From the perspective of the student, some students understand the concept but lack knowledge of how to apply it. The majority of students struggle to comprehend the data structure and are perplexed by it. Recent work focuses on how faculty members play a major role in active learning like developing models to explain the concept, conducting activities like role play, think-pair share, flipped classrooms and so on. In this work, a study was conducted in the course DSA which focused on reflective practice led by David Kolb's experiential learning theory. An experiment was conducted during Academic Year 2021-22 (Odd) in the course 18CS340 -- Data Structures and Algorithms for a set of 54 students. It is inferred that the student gained a higher or deeper knowledge level in this course and is confident to identify appropriate data structures for real world problems. By engaging in reflective practice, faculty members can think around and reflect on their experiences, learn from them, make changes, and enhance their learning and instructional skills. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Curriculum & Teaching Methodology / Maǧallaẗ al-Manāhiǧ wa-Turūq al-Tadrīs is the property of Arab Journal of Sciences & Research Publishing (AJSRP) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background/purpose: Effective evaluation of students' self-assessment ability is crucial. This study was to develop a standardized 3D printed teaching model accompanied by structured scoring rubrics for preclinical endodontic training, to appraise students' self-assessment ability, and to evaluate their perceptions of the training system. Methods: The 3D printing model was designed to house a standardized central incisor. Forty-four undergraduate dental students were enrolled and their endodontic performance was self-assessed and also rated by a teaching assistant and a tutor using 3 structured rubrics including access cavity, mechanical preparation, and obturation assessments. In total, 21 rubric evaluation items of preclinical tasks were assessed, and the assessment results from the student, teaching assistant, and tutor were compared. Furthermore, questionnaires were used to evaluate students’ experiences with the new system. Results: The tutor, teaching assistant, and students all had similar scorings and high intraclass correlation coefficients (ICC) for mechanical preparation and obturation rubrics, while the students had underestimated scores on the access cavity rubric with a rather low ICC of 0.387, which also lead to the underestimated overall scoring (P

A majority of government and international organisations have chosen English as their official language since it is the only language in the world with the status of a global language and a world language. One of the key components of learning a language is oral communication; however, students frequently overlook the value of oral communication, which reduces the effectiveness of language acquisition. To enhance the motivation and effectiveness of non-native English speakers to learn and practise English, this study aims to develop an interactive self-learning and self-improvement approach. A well-designed and chatbot-assisted learning environment is designed, using a variety of concepts such as informal learning, language learning, mobile learning, and educational mobile application, and evaluated. [ABSTRACT FROM AUTHOR]

This paper aims to develop an effective sensor fusion model for turning processes for the detection of tool wear. Fusion of sensors' data combined with novelty detection algorithm and learning vector quantisation (LVQ) neural networks is used to detect tool wear and present diagnostic and prognostic information. To reduce the number of sensors required in the monitoring system and support sensor fusion, the ASPS approach (Automated Sensor and Signal Processing Selection System) is used to select the most appropriate sensors and signal processing methods for the design of the condition monitoring system. The experimental results show that the proposed approach has demonstrated its efficacy in the implementation of an effective solution for the monitoring tool wear in turning. The results prove that the fusion of sensitive sensory characteristic features and the use of AI methods have been successful for the detection and prediction of the tool wear in turning processes and show the capability of the proposed approach to reduce the complexity of the design of condition monitoring systems and the development of a sensor fusion system using a self-learning method. [ABSTRACT FROM AUTHOR]

Modern manufacturing has to cope with dynamic and changing circumstances. Market fluctuations, the effects caused by unpredictable material shortages, highly variable product demand, and worker availability all require system robustness, flexibility, and resilience. To adapt to these new requirements, manufacturers should consider investigating, investing in, and implementing system autonomy. Autonomy is being adopted in multiple industrial contexts, but divergences arise when formalizing the concept of autonomous systems. To develop an implementation of autonomous manufacturing systems, it is essential to specify what autonomy means, how autonomous manufacturing systems are different from other autonomous systems, and how autonomous manufacturing systems are identified and achieved through the main features and enabling technologies. With a comprehensive literature review, this paper provides a definition of autonomy in the manufacturing context, infers the features of autonomy from different engineering domains, and presents a five-level model of autonomy — associated with maturity levels for the features — to ensure the complete identification and evaluation of autonomous manufacturing systems. The paper also presents the evaluation of a real autonomous system that serves as a use-case and a validation of the model. [ABSTRACT FROM AUTHOR]

Autonomy and self-learning are concepts that have regained value and meaning in modern methods of foreign language teaching. The purpose of this study is to investigate how language learners can be encouraged to gain autonomy in learning and self-learning skills in a French language classroom. To provide a solution in this regard, we first need to answer the question, what is the basic difference between selftaught and non-self-taught language learners? Our hypothesis is that self-taught learners naturally have higher "knowledge-skills", "knowledge-learning skills" and higher life experience than others. In order to compare the abilities of self-taught and non-self-taught learners in applying cognitive and metacognitive strategies, we published a call and asked 35 learners who had learned French completely or partially on their own to answer two questionnaires about using cognitive and metacognitive strategies. These two questionnaires are taken from Politzer, McGroarty and Willing. At the same time, 35 people who had not been self-taught in any course of learning French were asked to answer the above two questionnaires. The results of this study, which was presented as a descriptive statistic, show that learners use both strategies they have higher abilities. As a result, it should be said that autonomy and all cognitive and metacognitive strategies, although they may exist naturally in all individuals, but their emergence from potential to actual in most cases is not spontaneous and requires educational support. [ABSTRACT FROM AUTHOR]

This research investigates the listening issues and techniques that Chinese students deal with in English as a Second Language (ESL) listening comprehension lessons. This study employs semi-structured interviews with three Chinese students who are learning ELC at a Malaysian university. The study found that Chinese ELC students used certain self-learning techniques and had issues that needed to be addressed before, during, and after listening. As a result, this study will provide teachers with crucial knowledge about how to support these students' ELC in a way that is effective, efficient, and enjoyable. [ABSTRACT FROM AUTHOR]

The synchronous reluctance motor (SynRM) has significant nonlinear characteristics due to the problems of magnetic saturation and cross-coupling and the poor adaptability of the general controller to parameter changes seriously affects the control performance of the motor. In order to solve the above problems, this paper proposed a control system for the SynRM with a brain emotion controller based on empirical information to solve the motor control problem of magnetic saturation. Firstly, the nonlinear mathematical model of the SynRM considering magnetic saturation is established by introducing the magnetic saturation parameter. Secondly, the sensory input function and emotional cue function based on systematic error are given and the vector control system of the SynRM considering magnetic saturation is designed. The influence of the parameters and the learning rate of the brain emotional learning based intelligent controller (EI-BELBIC) on the adjustment range of the controller parameters is studied. Then the SynRM is controlled under different working conditions and the control effect is observed. The results show that the designed vector control system of the SynRM based on EI-BELBIC has strong reliability, accurate control, rapid response, and strong anti-interference ability under magnetic saturation. [ABSTRACT FROM AUTHOR]

The dramatic growth of smart devices and the Internet of things (IoT) has increased the volume of exchanges and data on the web. The centralized and traditional architecture of cloud computing does not meet the demands of users and proper implementation of latency-sensitive applications due to latency and mass demands of IoT applications that have different needs compared to existing applications. As a result, edge computing has been presented for collecting and processing of data generated by these objects, which facilitates data processing with low latency and close to users at the edge of the network. Its main purpose is to bring computational resources and storage close to the end-user on the network. As far as the storage capacity of fog nodes is limited, the proper use of fog node resources significantly influences their performance. In this paper, a framework based on control MAPE-K loop has been used to obtain the optimal state in workload balance. The users' workload forecasting model is a combination of linear regression and support vector regression methods and offers better performance compared to the conventional reactive self-assessment methods. In the planning phase, a fuzzy self-learning algorithm is used to determine the automated scale of resource provisioning policy. By comparing three criteria of load delay, cost, and amount of consumed energy in the proposed method and recent works, the proposed method has been able to balance all three criteria optimally. [ABSTRACT FROM AUTHOR]

Foreign languages play a special role in the process of forming intercultural communicative and professional competence. Currently, the main goal of a foreign language course in university is determined by teaching it as a means of intercultural communication in the field of professional activity. The modern credit-module system of higher education assumes that a significant part of the study time is allocated to independent work of students. The tendency to increase the amount of time devoted to independent mastering of educational and professional programmes, the concepts of continuous education and lifelong learning are oriented towards the student as an active subject of their own knowledge acquisition process. Self-management in education opens up the great potential of self-learning as one of the trends in lifelong education. This is especially relevant when it comes to learning foreign languages. Researchers of the problems of teaching foreign languages in higher education, highlighting modern trends in the development of education, emphasize the approach to perception of the educational levels obtained in the process of formal education not as a completed process, but as one of the stages in the continuity of education. [ABSTRACT FROM AUTHOR]

Copyright of Electric Power Engineering Technology is the property of Editorial Department of Electric Power Engineering Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Control augmentation systems (CAS) are conventionally built with classical controllers which have the following drawbacks: dependence on domain specific knowledge for tuning and limited self-learning capability. Consequently, these drawbacks lead to sub-optimal aircraft stability and performance when exposed to time varying disturbances. Hence, to curb the stated problems; this paper proposes the development of a deep reinforcement learning (DRL) pitch-rate CAS (qCAS), aimed at guaranteeing adaptive stability, pitch-rate control tracking and disturbance rejection across the longitudinal dynamics of an aircraft. This stated aim was actualized by developing a CAS with a deep deterministic policy gradient (DDPG) agent. Subsequently, this proposed method was compared with two classical qCAS methods (a developed PID-aCAS and a benchmark PIqCAS obtained from literature). The results show that the developed DDPG-qCAS method outperformed the classical methods in peak overshoot, reference command tracking and disturbance rejection as well as mean absolute error (MSE) and mean steady state error (MSSE). Hence, it can be inferred that it is important to apply artificially intelligent controllers to the flight control systems of aircraft in order to achieve superior time response, control command tracking accuracy and disturbance rejection. [ABSTRACT FROM AUTHOR]

Abstract Background The use of head mounted display (HMD)-based immersive virtual reality (VR) coaching systems (HMD-VRC) is expected to be effective for skill acquisition in radiography. The usefulness of HMD-VRC has been reported in many previous studies. However, previous studies have evaluated the effectiveness of HMD-VRC only through questionnaires. HMD-VRC has difficulties in palpation and patient interaction compared to real-world training. It is expected that these issues will have an impact on proficiency. The purpose of this study is to determine the impact of VR constraints in HMD-VRC, especially palpation and patient interaction, on radiographic skills proficiency in a real-world setting. Methods First-year students (n = 30) at a training school for radiology technologists in Japan were randomly divided into two groups, one using HMD-VRC (HMD-VRC group) and the other practicing with conventional physical equipment (RP group) and trained for approximately one hour. The teachers then evaluated the students for proficiency using a rubric method. Results In this study, it was found that some skills in the HMD-VRC group were equivalent to those of the RP group and some were significantly lower than those of the RP group. There was a significant decrease in proficiency in skills related to palpation and patient interaction. Conclusions This study suggests that HMD-VRC can be less effective than real-world training in radiographic techniques, which require palpation and patient interaction. For effective training, it is important to objectively evaluate proficiency in the real world, even for HMD-VRC with new technologies, such as haptic presentation and VR patient interaction. Trial registration The study was conducted with the approval of the Ethics Committee of International University of Health and Welfare (Approval No.21-Im-035, Registration date: September 28, 2021).

The original vibration signals of the fault gear under different working conditions have a large distribution difference, and there will be insufficient feature extraction during fault diagnosis, which leads to the problem of low diagnostic accuracy. Therefore, a self-learning model based on residual shrinkage network (SLRSN) is proposed. The model constructs a deep residual shrinkage network as the main network for feature extraction of the original vibration signal to enhance the robustness of the model. Then self-believing loss and self-doubting loss are proposed to achieve self-confidence and suspicion of health status prediction. The first is self-confidence loss, which adopts sub-domain distribution adaptation to actively align learned cross-domain features. The second is self-doubt loss, which provides SLRSN with the ability to extricate from wrong experience. Finally, to mitigate the effects of negative transfer, a novel adaptative weight allocation mechanism is designed to recalibrate the weighting of each source domain sample. Through the experiment of two gearboxes, it is verified that the proposed SLRSN method has good diagnostic reliability under the condition of gear speed and load change. [ABSTRACT FROM AUTHOR]

This study investigates the factors influencing ChatGPT acceptance in self-directed English language learning and explores its potential to enhance the learning process. A multi-methods approach was employed, comprising a systematic review of 40 empirical articles, a quantitative survey of 344 English as a Foreign Language (EFL) learners, and 19 semi-structured interviews. Results indicated that interactivity, enjoyment, trust, and subjective norms significantly affect continued use through perceived usefulness, while human-likeness, self-efficacy, and technology anxiety influence it through perceived ease of use. EFL learners utilize ChatGPT for both primary and secondary English learning purposes, supporting various language dimensions including reading, writing, vocabulary, and grammar. The study also identified both passive and active language learning engagement facilitated by ChatGPT. This research contributes to the field by proposing an extended Technology Acceptance Model and a conceptual framework for chatbot-assisted language learning. For chatbot developers, the findings underscore the need to enhance interactivity, human-likeness, enjoyment, and trust while minimizing technology anxiety to increase sustained use. Educators can utilize these insights to better integrate ChatGPT into their curricula, supporting diverse language skills and engaging learners more effectively. Together, these findings provide concrete guidance for enhancing the design and implementation of AI chatbots in language learning contexts. [ABSTRACT FROM AUTHOR]

The present study introduces a new self-learning framework based on a recurrent neural network to compute the nonlinear viscoplastic response of plate structures undergoing isotropic damage. The neural network comprising Legendre Memory Unit (LMU) cells and dense transformations is pretrained with a combination of data-driven and physics-based loss functions. Once pretrained, the model is deployed in the explicit integration scheme to predict the strain increment in the thickness direction satisfying plane stress condition. This results in elimination/reduction in the iterations required to fulfill the plane stress condition and leads to faster convergence since the neural network replaces the root-finding methods. The proposed self-learning method differentiates itself from the well-known Physics-Informed Neural Networks (PINNs) since the neural network model self-learns online through the proposed physics-based loss term without the need for data. We compare the results of the Neural Network-enhanced integration scheme to validate the proposed method with those of the classical explicit integration scheme. • The LMU-based model acts as a nonlinear self-learning solver to satisfy the plane stress condition σ 33 = 0. • The iterations required to achieve the plane stress condition are reduced/eliminated. • The NN model receives constant feedback from the physics-based loss function ensuring convergence. • The internal variables of the LMU model trace the evolution of σ 33 = 0 in the nonlinear domain for each Gaussian point. [ABSTRACT FROM AUTHOR]

Text annotation is an essential element of the natural language processing approaches. The manual annotation process performed by humans has various drawbacks, such as subjectivity, slowness, fatigue, and possibly carelessness. In addition, annotators may annotate ambiguous data. Therefore, we have developed the concept of automated annotation to get the best annotations using several machine-learning approaches. The proposed approach is based on an ensemble algorithm of meta-learners and meta-vectorizer techniques. The approach employs a semi-supervised learning technique for automated annotation to detect hate speech. This involves leveraging various machine learning algorithms, including Support Vector Machine (SVM), Decision Tree (DT), K-Nearest Neighbors (KNN), and Naive Bayes (NB), in conjunction with Word2Vec and TF-IDF text extraction methods. The annotation process is performed using 13,169 Indonesian YouTube comments data. The proposed model used a Stemming approach using data from Sastrawi and new data of 2245 words. Semi-supervised learning uses 5%, 10%, and 20% of labeled data compared to performing labeling based on 80% of the datasets. In semi-supervised learning, the model learns from the labeled data, which provides explicit information, and the unlabeled data, which offers implicit insights. This hybrid approach enables the model to generalize and make informed predictions even when limited labeled data is available (based on self-learning). Ultimately, this enhances its ability to handle real-world scenarios with scarce annotated information. In addition, the proposed method uses a variety of thresholds for matching words labeled with hate speech ranging from 0.6, 0.7, 0.8, to 0.9. The experiments indicated that the DT-TF-IDF model has the best accuracy value of 97.1% with a scenario of 5%:80%:0.9. However, several other methods have accuracy above 90%, such as SVM (TF-IDF and Word2Vec) and KNN (Word2Vec), based on both text extraction methods in several test scenarios.

Complex and high-computational-cost algorithms are usually the state-of-the-art solution for autonomous driving cases in which non-holonomic robots must be controlled in scenarios with spatial restrictions and interaction with dynamic obstacles while fulfilling at all times safety, comfort, and legal requirements. These highly complex software solutions must cover the high variability of use cases that might appear in traffic conditions, especially when involving scenarios with dynamic obstacles. Reinforcement learning algorithms are seen as a powerful tool in autonomous driving scenarios since the complexity of the algorithm is automatically learned by trial and error with the help of simple reward functions. This paper proposes a methodology to properly define simple reward functions and come up automatically with a complex and successful autonomous driving policy. The proposed methodology has no motion planning module so that the computational power can be limited like in the reactive robotic paradigm. Reactions are learned based on the maximization of the cumulative reward obtained during the learning process. Since the motion is based on the cumulative reward, the proposed algorithm is not bound to any embedded model of the robot and is not being affected by uncertainties of these models or estimators, making it possible to generate trajectories with the consideration of non-holonomic constrains. This paper explains the proposed methodology and discusses the setup of experiments and the results for the validation of the methodology in scenarios with dynamic obstacles. A comparison between the reinforcement learning algorithm and state-of-the-art approaches is also carried out to highlight how the methodology proposed outperforms state-of-the-art algorithms.

Resumen El estudio presenta la experiencia docente llevada a cabo en varios grupos de laboratorio de la Asignatura de Expresión Gráfica en estudiantes de ingeniería. La investigación tiene como finalidad evaluar la percepción que tienen los alumnos al revisar las prácticas mediante videos audiovisuales en formato screencasts y la plataforma virtual Poliformat y e-mail. Los resultados de las encuestas nos ayudan a enfocar una solución tecnológica de retroalimentación virtual del profesor que, aunque es general potencia el aprendizaje autónomo y flexibiliza la gestión del tiempo del alumno. Siendo positiva la percepción de los estudiantes al empleo de herramientas tecnológicas como medio de feedback para la revisión de las prácticas. Abstract The paper describes a teaching experience carried out among various laboratory groups of Engineering students in the subject Graphic Expression. The aim was to evaluate the students’ perception on reviewing the lab practices by means of Screencast format videos, the Poliformat Platform and e-mail. The results of the surveys helped us to create a teacher’s virtual technological feedback solution which, although general, improved autonomous learning and gave students more flexibility in the use of their time. They also obtained a positive perception from using technological tools to obtain feedback for reviewing the practices.